Principle Component Analysis(PCA) [1]}, is a suitable method to perform linear dimensionality reduction on such correlated features. PCA is a statistical procedure which converts a set of observations (for possibly correlated variables), into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance, and each succeeding component in turn has the highest variance under the key constraint, that it is orthogonal to the preceding components. However, if we apply PCA on the whole dataset (all features), the resulting principle component will be difficult to interpret. Therefore, we decided to apply PCA on each category of features individually, exploiting the latent similarities within the categories. The approach tries to balance the necessity of dimensionality reduction, while allowing for interpretation of the principal components. A cut-off value \(c\)  was used for explained variance, to determine the number of components for each category.
